Leveraging spatial abstraction in traffic analysis and forecasting with visual analytics

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Andrienko, N. ; Andrienko, G. ; Rinzivillo, S. (2016)

A spatially abstracted transportation network is a graph where nodes are territory compartments (areas in geographic space) and edges, or links, are abstract constructs, each link representing all possible paths between two neighboring areas. By applying visual analytics techniques to vehicle traffic data from different territories, we discovered that the traffic intensity (a.k.a. traffic flow or traffic flux) and the mean velocity are interrelated in a spatially abstracted transportation network in the same way as at the level of street segments. Moreover, these relationships are consistent across different levels of spatial abstraction of a physical transportation network. Graphical representations of the flux-velocity interdependencies for abstracted links have the same shape as the fundamental diagram of traffic flow through a physical street segment, which is known in transportation science. This key finding substantiates our approach to traffic analysis, forecasting, and simulation leveraging spatial abstraction.\ud \ud We propose a framework in which visual analytics supports three high-level tasks, assess, forecast, and develop options, in application to vehicle traffic. These tasks can be carried out in a coherent workflow, where each next task uses the results of the previous one(s). At the 'assess' stage, vehicle trajectories are used to build a spatially abstracted transportation network and compute the traffic intensities and mean velocities on the abstracted links by time intervals. The interdependencies between the two characteristics of the links are extracted and represented by formal models, which enable the second step of the workflow, 'forecast', involving simulation of vehicle movements under various conditions. The previously derived models allow not only prediction of normal traffic flows conforming to the regular daily and weekly patterns but also simulation of traffic in extraordinary cases, such as road closures, major public events, or mass evacuation due to a disaster. Interactive visual tools support preparation of simulations and analysis of their results. When the simulation forecasts problematic situations, such as major congestions and delays, the analyst proceeds to the step 'develop options' for trying various actions aimed at situation improvement and investigating their consequences. Action execution can be imitated by interactively modifying the input of the simulation model. Specific techniques support comparisons between results of simulating different "what if" scenarios.
  • References (68)
    68 references, page 1 of 7

    [1] S. Afzal, R. Maciejewski, and D.S. Ebert. Visual analytics decision support environment for epidemic modeling and response evaluation. In Proc. IEEE Conf. Visual Analytics Science and Technology (VAST'2011), pp. 191-200, 2011.

    [2] G. Andrienko, N. Andrienko, P. Bak, D. Keim, and S. Wrobel. Visual Analytics of Movement. Springer, 2013.

    [3] G. Andrienko, N. Andrienko, S. Rinzivillo, M. Nanni, D. Pedreschi, and F. Giannotti, Interactive Visual Clustering of Large Collections of Trajectories, In Proc. IEEE Symp. Visual Analytics Science and Technology (VAST'09), pp. 3-10, 2009.

    [4] N. Andrienko and G. Andrienko, Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach, Springer, Berlin, 2006.

    [5] N. Andrienko and G. Andrienko, Spatial Generalization and Aggregation of Massive Movement Data, IEEE Trans. Visualization and Computer Graphics, 17(2): 205-219, 2011.

    [6] N. Andrienko and G. Andrienko. Visual analytics of movement: An overview of methods, tools and procedures. Information Visualization, 12(1): 3-24, 2013.

    [7] N. Andrienko and G. Andrienko, A Visual Analytics Framework for Spatio-temporal Analysis and Modeling, Data Mining and Knowledge Discovery, 27(1): 55-83, 2013.

    [8] N. Andrienko, G. Andrienko, and S. Rinzivillo. Exploiting Spatial Abstraction in Predictive Analytics of Vehicle Traffic. ISPRS International Journal of Geo-Information, 4(2): 591-606, 2015.

    [9] P. Bak, M. Marder, S. Harary, A. Yaeli, and H.J. Ship, Scalable Detection of Spatiotemporal Encounters in Historical Movement Data, Computer Graphics Forum, 31(3-31): 915-924, June 2012.

    [10]P. Bak, E. Packer, H. Ship, and D. Dotan, Algorithmic and Visual Analysis of Spatiotemporal Stops in Movement Data. In Proceedings of the 20th ACM SIGSPATIAL International Con-ference on Advances in Geographic Information Systems (ACM GIS 2012), November 6-9, 2012. Redondo Beach, CA, USA, 2012.

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